AI Mathematical Olympiad - Progress Prize 3 ~ 91st Place Solution

Solving olympiad-level mathematical reasoning problems using open-source LLM inference pipeline and competitive reasoning workflow.

Project Duration: April 01, 2026 - April 15, 2026

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File Structure

├── INFO.md                      # Competition details, evaluation, rules and timeline
├── README.md
├── aimo-inference.ipynb         # Main competition inference pipeline
├── dependency-install.ipynb     # Dependency installation and environment setup
├── inference.py                 # Standalone inference script
├── utils.ipynb                  # Utility experiments and helper workflows
├── certificate.png              # Certificate of Achievement from Kaggle
├── assets
│   └── eval_info.png            # helper image
└── output
    └── submission.parquet       # Best submission file

Installation

git clone https://github.com/krishnaura45/aimo-problem-solving.git
cd aimo-problem-solving

Usage

# Open notebook workflow
jupyter notebook aimo-inference.ipynb

# or run standalone inference
python inference.py

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Problem Statement

The goal of the AI Mathematical Olympiad - Progress Prize 3 Kaggle competition was to create open-source algorithms capable of solving olympiad-level mathematical reasoning problems written entirely in LaTeX notation.

The competition featured highly challenging problems spanning:

• Algebra
• Combinatorics
• Geometry
• Number Theory

with difficulty ranging from national olympiad level up to IMO-standard mathematics.

Hosted on Kaggle, the challenge evaluated submissions using a specialized penalized accuracy framework across public and hidden private reruns. Each problem required predicting a non-negative integer answer between 0 and 99999.

The competition emphasized genuine mathematical reasoning capabilities and robust inference under constrained notebook environments.

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Approach

Inference Pipeline

The primary workflow was implemented in:

• aimo-inference.ipynb
• inference.py

The overall pipeline focused on efficient large language model inference under strict Kaggle notebook constraints.

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Problem Processing

• Processed olympiad-style mathematical problems written in LaTeX
• Structured prompts for reasoning-oriented inference

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Reasoning Workflow

The inference workflow emphasized:

• Multi-step mathematical reasoning
• Symbolic interpretation of problem statements
• Integer answer extraction and normalization

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Competition Results

• Announced on: May 12, 2026.

• Public/Private Leaderboard Scores:

  • 37
  • 38
  • 39
  • 40
  • 42
  • 43

• Performance:

  • Best Private Score: 43.0
  • Placed 91st out of 4066 participants and 4138 teams as a solo participant.

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References

• Dataset: AIMO - PP3
• Competition Info: INFO.md
• Main Inference Notebook: aimo-inference.ipynb
• Official Models Page: https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-3/models

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Tech Stack

• Language: Python
• Libraries / Frameworks:
  • transformers
  • torch
  • pandas
  • numpy
• Techniques:
  • LLM Inference
  • Mathematical Reasoning
  • Sequential API-based Evaluation
  • Prompt-based Solving
• Tools:
  • Jupyter Notebook
  • Kaggle Notebooks
  • GPU-based inference environments

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📌 This project demonstrates the growing capability of open-source reasoning systems in solving olympiad-level mathematical problems under competitive inference constraints.